17937664. METHODS AND SYSTEMS FOR VERIFICATION OF MACHINE LEARNING-BASED VARNISH ANALYSIS simplified abstract (Ford Global Technologies, LLC)
Contents
- 1 METHODS AND SYSTEMS FOR VERIFICATION OF MACHINE LEARNING-BASED VARNISH ANALYSIS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHODS AND SYSTEMS FOR VERIFICATION OF MACHINE LEARNING-BASED VARNISH ANALYSIS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.9.1 Unanswered Questions
- 1.9.2 How does the deep learning tool process and analyze the images to estimate varnish fill percentages?
- 1.9.3 What are the specific adjustments that may be recommended for the imaging setup based on the comparison of estimated varnish fill percentages with predetermined values?
- 1.10 Original Abstract Submitted
METHODS AND SYSTEMS FOR VERIFICATION OF MACHINE LEARNING-BASED VARNISH ANALYSIS
Organization Name
Inventor(s)
Robert Schroeter of Livonia MI (US)
METHODS AND SYSTEMS FOR VERIFICATION OF MACHINE LEARNING-BASED VARNISH ANALYSIS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17937664 titled 'METHODS AND SYSTEMS FOR VERIFICATION OF MACHINE LEARNING-BASED VARNISH ANALYSIS
Simplified Explanation
The abstract describes a method and system for verifying a deep learning tool used to evaluate the varnish condition of a stator. The method involves processing images of replicas of a stator section with different varnish fill percentages to estimate varnish fill percentages using the deep learning tool.
- Verification of deep learning tool for evaluating varnish condition of a stator
- Method involves processing images of stator replicas with different varnish fill percentages
- Deep learning tool analyzes images to estimate varnish fill percentages
- Comparison of estimated varnish fill percentages with predetermined values
- Notification recommending further training or adjustments based on comparison
Potential Applications
The technology can be applied in industries where stators are used, such as in electric motors, generators, and other rotating machinery.
Problems Solved
This technology helps in accurately evaluating the varnish condition of stators, which is crucial for maintaining the efficiency and performance of electric motors and generators.
Benefits
- Improved accuracy in assessing varnish condition - Potential for early detection of varnish issues - Efficient maintenance planning for stators
Potential Commercial Applications
- Electric motor manufacturing companies - Generator maintenance service providers - Industrial machinery maintenance companies
Possible Prior Art
One possible prior art could be the use of traditional image processing techniques for evaluating varnish condition in stators. However, the use of deep learning tools for this purpose may be a novel approach.
Unanswered Questions
How does the deep learning tool process and analyze the images to estimate varnish fill percentages?
The abstract does not provide specific details on the methodology or algorithms used by the deep learning tool for processing and analyzing the images.
What are the specific adjustments that may be recommended for the imaging setup based on the comparison of estimated varnish fill percentages with predetermined values?
The abstract mentions adjustments to the imaging setup as a recommendation based on the comparison results, but it does not specify what these adjustments may entail.
Original Abstract Submitted
Methods and systems are provided for verifying a deep learning tool for evaluating a varnish condition of a stator. In one example, a method for verifying the deep learning tool includes receiving images of replicas of a stator section at a processor of a computing system, the replicas of the stator section having different predetermined varnish fill percentages. The images are process and analyzed by the deep learning tool to output estimated varnish fill percentages. The estimated varnish fill percentages may be compared to the predetermined varnish fill percentages and a notification recommending at least one of further training of the deep learning tool and adjustments to an imaging setup for acquiring the images.
